Hadoop Framework Guide 2022

By Akssar

Last updated on Feb 25 2022

Hadoop Framework Guide 2022

Hadoop Framework - Introduction, Components, and Uses

 

If you are learning about Big Data you are bound to come across mentions of the “Hadoop framework”. The rise of big data and its analytics have made the Hadoop framework very popular. Hadoop is open-source software, meaning the bare software is easily available for free and customizable according to individual needs. This helps in curating the software according to the specific needs of the big data that needs to be handled. As we know, big data is a term used to refer to the huge volume of data that cannot be stored or processed, or analyzed using the mechanisms traditionally used. It is due to several characteristics of big data. This is because big data has high volume and is generated at great speed and the data comes in many varieties.

Since the traditional frameworks are ineffective in handling big data, new techniques had to be developed to combat it. This is where the Hadoop framework comes in. Hadoop framework is based on java primarily and is used to deal with big data.

 

What is Hadoop?

Hadoop is a data handling framework written in Java, primarily, with some secondary code in shell script and C. It uses a basic-level programming model and is able to deal with large datasets. It was developed by Doug Cutting and Mike Cafarella. This framework uses distributed storage and parallel processing to store and manage big data. It is one of the highest used software to handle big data. Hadoop mainly consists of three components, Hadoop HDFS, Hadoop MapReduce, and Hadoop YARN. These components come together to handle big data effectively. These components are also known as Hadoop modules.

Hadoop is slowly becoming a mandatory skill required from a data scientist. Companies looking to invest in Big Data technology are increasingly giving more importance to Hadoop making it a valuable skill upgrade for professionals. Hadoop 3. x is the latest version of Hadoop.

 

How Does Hadoop Work?          

The idea behind Hadoop is rather simple. Big data presents challenges in the form of volume, variety, and velocity. It would not be practical to build bigger and bigger servers with heavy configurations that can handle such a large data pool. However, as an alternative, it would be easier to tie together many computers with a single CPU. This would make it a distributed system that functions under a single system. This means that the clustered computers can function parallelly towards the same goal. This would make the process of handling big data both faster and cheaper.

This can be better understood with the help of an example. Imagine a carpenter who primarily makes chairs and stores them at his warehouse before being sold. At a point, the market demand for other products like a table and cupboard arises. So now the same carpenter is working on all three products. However, this is depleting his energy and he is not able to keep up producing all three. He decides to enlist the help of two other apprentices who each work on one product. Now they are able to produce at a good rate but a problem regarding storage arises. Now the carpenter cannot buy a bigger and bigger warehouse as per increases in demand or product. Instead, he takes three smaller storages for the three different products.

In this analogy, we can see the carpenter as the server handling data. The rise in demand, that is, in the variety, velocity, and volume, of the product, makes it big data, which is too much for the server to handle alone. Now the hiring of two apprentices working under him is the idea of a single CPU helped by multiple computers, therefore they are working toward the same goal. To avoid a bottleneck in storage, curated storage as per variety is assigned. This is the gist of how Hadoop works.

 

Main Components of Hadoop Framework

There are three core components of Hadoop as mentioned earlier. They are HDFS, MapReduce, and YARN. These together form the Hadoop framework architecture.

  • HDFS (Hadoop Distributed File System):

It is a data storage system. Since the data sets are huge, it uses a distributed system to store this data. It is stored in blocks where each block is 128 MB. It consists of NameNode and DataNode. There can only be one NameNode but multiple DataNodes.

Features:

  • The storage is distributed to handle a large data pool
  • Distribution increases data security
  • It is fault-tolerant, other blocks can pick up the failure of one block
  • MapReduce:

The MapReduce framework is the processing unit. All data is distributed and processed parallelly. There is a MasterNode that distributes data amongst SlaveNodes. The SlaveNodes do the processing and send it back to the MasterNode.

Features:

  • Consists of two phases, Map Phase and Reduce Phase.
  • Processes big data faster with multiples nodes working under one CPU

 

  • YARN (yet another Resources Negotiator):

It is the resource management unit of the Hadoop framework. The data which is stored can be processed with help of YARN using data processing engines like interactive processing. It can be used to fetch any sort of data analysis.

Features:

  • It is a filing system that acts as an Operating System for the data stored on HDFS
  • It helps to schedule the tasks to avoid overloading any system

 

Advantages of the Hadoop framework

Hadoop framework has become the most used tool to handle big data because of the various benefits that it offers.

 

  • Data Locality:

The concept is rather simple. The pool of data is very large and it would be very slow and tiresome to move the data to the computation logic. By using, if data locality, the computation logic can instead be moved toward the data. This makes processing much faster.

 

  • Faster Data Processing:

As we saw earlier, the data is stored in small blocks using the HDFS filing system. This makes it possible to process the data parallelly using the common CPU with the help of MapReduce. This makes the performance level very high when compared to any traditional system.

 

  • Inbuilt fault tolerance:

The problem with using smaller cluster computers is that the risk of them crashing is very real. This is solved with the help of a high fault tolerance level which is inbuilt into the Hadoop platform. This is because of the various DataNodes that are present. This, along with parallel data processing and storage ensures that data is available in multiple nodes, which ensures that these systems can take over and provide cover for any system that crashes. Hadoop in fact makes 3 copies of each file block. This ensures that any fault in the system is tolerated.

 

  • High Availability:

This refers to the high and easy availability of data on the Hadoop cluster. Due to the high fault tolerance that is inbuilt, the data is reliable, easily available, and can be accessed easily. processed data can be easily accessed using YARN as well.

 

  • Highly Scalable:

This basically refers to the flexibility one has in scaling up or down, the machines or nodes used for data processing. Since multiple machines are used parallelly under the same CPU it is possible. Scaling is done according to changes in the volume of data or requirements of the organization.

 

  • Flexibility:

Hadoop framework is written in Java and C, it can be easily run on any system. Further, it can be curated to suit the specific needs of the type of data. It can handle both structured and unstructured data efficiently. It can handle very different kinds of data sets ranging from social media analysis to data warehousing.

 

  • Open Source:

It means it is free to use. Since it is an open-source project the source code is available online for anyone to make modifications to. This allows the Hadoop software to be curated according to very specific needs.

 

  • Easy to Use:

Hadoop is easy to use since the developers need not worry about any of the processing work since it is managed by Hadoop itself. Hadoop ecosystem is also very large comes up with lots of tools like Hive, Pig, Spark, HBase, Mahout, etc.

 

  • Cost-Effective:

Not only is it highly efficient and customizable, but it also reduces the cost of processing such data significantly. Traditional data processing would require investments in very large server systems for a less efficient model. This framework instead employs cheaper to invest systems to deliver a very efficient system. This makes it highly preferred by organizations.

 

Conclusion

Hadoop framework has become one of the most used frameworks for handling big data for a reason. The Hadoop platform and application framework offered by it has made big data analysis more efficient as well as cost-effective. It is fast becoming one of the most sought-after skill sets by recruiters and will soon be a mandatory requirement for data scientists. It would be prudent to get a certification for the same if you are looking to upskill and are working with data science or big data analytics. Taking the help of a recognized training body like Sprintzeal will help you a great deal in this regard. Wait no more and take the help of Sprintzeal and get certified in Hadoop now!

 

Popular Big Data and Hadoop Courses:

Big Data Hadoop Certification Training Course

Big Data Analytics Training Course

 

Some articles that might intrigue you –

BIG DATA GUIDE 2022

HADOOP INTERVIEW QUESTIONS AND ANSWERS 2022

 

About the Author

Sprintzeal   Akssar

A law graduate with an immense passion for research and writing. Loves to travel, read and eat. When not doing that, loves working toward bringing well-researched and informative content to readers. Has experience in, and, is passionate about journalistic pieces, blog posts, review articles, sports coverage, technical research pieces, script-writing, website content, social media marketing, advertising, and creative writing. Sleeps when the ink runs out writing all that.

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